But since you don't, why not calculate the density directly for each
point (e.g., in a loop). The formula is simple enough:
mean(dnorm(x-gx,sd=h1)*dnorm(y-gy,sd=h2))
yes, this was my initial thought, and it works. (But i was hoping kde2d or
some similar function could do it faster). I'l try
On Tue, 16 Feb 2010 03:36:17 -0800 (PST) geir
wrote:
> I want the density estimates for the points in a k x 2 matrix like for
> example
>
> A=[(0,7,0.3),(0.1,0.2),...,(0.5,0.9)]^T
>
> which is not equally spaced, (and i do not need the density of every
> combin
Trafim Vanishek posted a similar problem: "Joint density approximation?"
(without any solution for kde2d). Here is an example to illustrate my
problem.
Originally data is for example:
a=runif(10) (yes, the number of data should be larger)
b=runif(10)
c=kde2d(a,b,n=10,lims=c(0,1,0,1))
attach(c
On Feb 15, 2010, at 10:02 AM, geir wrote:
Sorry, i did indeed mean function - not package.
kde2d lets you determine the grid - you choose "n" and "lims", which
gives
you and n x n matrix for which the density is estimated (Seems like
the
output is meant for graphical purposes). But i have
Sorry, i did indeed mean function - not package.
kde2d lets you determine the grid - you choose "n" and "lims", which gives
you and n x n matrix for which the density is estimated (Seems like the
output is meant for graphical purposes). But i have a matrix consisting of
points (x,y) where i want
On Feb 15, 2010, at 8:08 AM, geir wrote:
Problem:
Based on a n x 2 data matrix i want a kernel estimate of the bivariate
density.
However, i also wish to specify wich points the density should be
calculated at.
I can offcourse just write the full kernel density estimate as a R-
code, but
Problem:
Based on a n x 2 data matrix i want a kernel estimate of the bivariate
density. However, i also wish to specify wich points the density should be
calculated at.
I can offcourse just write the full kernel density estimate as a R-code, but
surely there must already exist some package for
7 matches
Mail list logo
